How much has Cuban productivity increased since 1960?

Is it possible for two equally rich countries (on a per capita basis) to have different level of output per worker? The answer is obviously yes, and it matters in the case of measuring growth in Cuba since the revolution.

A country with a very young population will tend to have fewer workers than one with an older (but not too old) population. Let’s say that countries A and B have a median age of 22.5 in year one.  However, in year ten, country A has a median age of 35 but country B has seen a more modest increase to a median age of 25. This will bias any estimates of growth comparison between both country. The increase in the median age suggests that there are more and more workers in country A (people of prime age) than in country B. As a result of that, output per capita will increase faster in country A than in country B even if both countries have equal rates of growth in output per worker.

Well, countries A and B are basically Cuba and most of the rest of Latin America. Since the 1950s, Cuba’s population has aged rapidly but birth rates have plummeted so fast that families shrunk. With fewer kids in the population, it means that the share of the Cuban population that are of prime working age increased rapidly. This is what biases the comparison of Cuban living standards with other Latin American countries.

In the figure below, I took the GDP (the Maddison data) of Cuba since 1950 (indexed at 1960 to see the arrival of Castro) and divided it by the total population, the population above 15 years of age and the population between 15 and 64.


As one can see, with the GDP per capita series, Cubans saw a 50% increase in their incomes between 1960 and 2005 (the Maddison data stops at 2008). However, when you look at GDP per working age adult in order to capture the growth in productive capacity, you get moderately different results whereby the cumulative increase is three-fifths to half as small.

In light of this, it seems like Cuba’s living standards are less and less impressive.

What is Wrong with Income Inequality? Five Reasons to be Concerned

I sometimes part ways with many of my libertarian and classical liberal friends in that I do have some amount of tentative concern for income/wealth inequality (for the purposes of this article, the otherwise important economic distinction between the two is not particularly relevant since the two are strongly correlated with each other). Many libertarians argue that inequality ultimately doesn’t matter. There is good reason to think this drawing from the classic arguments of Nozick and Hayek about how free exchange in a market economy can often interrupt preferred distributions.

The argument goes like this: take whatever your preferred distribution of income is, be it purely egalitarian or some sort of Rawlsian distribution such that the distribution benefits only the worst off in society. Assume there is one individual in the economy who has some product or service everyone wants to buy (in Nozick’s example it was Wilt Chamberlain playing basketball), and let everyone pay a relatively small amount of income to that one individual. For example, assume you have a society with 10,000 people all who start off with an equal endowment of $5 and all of them decide to pay Wilt Chamberlain $1 to watch him play basketball. Very few people would object to those individual exchanges, yet at the end Wilt Chamberlain ends up with $10,005 dollars and everyone else has $4, and our preferred distribution of income has been grossly upset even though the individual actions that led to that distribution are not objectionable. In other words, allowing for free exchange precludes trying to construct an optimal result of that free exchange (a basic consequence of recognizing spontaneous order).

Further, these libertarians argue, it is more important to ensure that the poor are better off in absolute terms than to ensure they are better off relative to their wealthier peers. Therefore, if a given policy will increase the wealth of the wealthiest by 10% and the poorest by 5%, there is no reason to oppose this policy on the grounds that it increases inequality because the poor are still made richer. Therefore, it is claimed, we should focus on policies that improve economic growth and the incomes of the poor and be indifferent as to its impact on relative inequality, since those policies are strongly correlated with bettering the economic conditions of the poor. In fact, as Mises Argued in Liberalism and the Classical Tradition, a certain amount of inequality is necessary for markets to function: they create a market for luxury goods that can be experimented and developed into future mass-consumption goods everyone can consume. Not everyone could afford, for an example, an IPod when it first came out, however today MP3 players are cheap and plentiful because the very wealthy were able to demand it when it was very expensive.

I agree with my libertarians in thinking that this argument is largely correct, however I do not think it proves, as Hayek argued, that social justice (understood in this context as distributive justice) is a “mirage” or that we should be altogether unconcerned with wealth or income distributions. All this argument does is mean that there is no overall deontological theory for an ideal income distribution, but there still might be good consequentialist reasons to think that excessively unequal distributions can impact many of the things that classical liberals tell us to worry about, such as the earnings of the poor, more free political economic outcomes, or overall economic growth. Further, even on Nozick’s entitlement theory of justice, we might oppose income inequality if it arises through unjust means. Here are five reasons why libertarians and classical liberals should be concerned about income inequality (note that they are mostly empirical reasons, not claims about the nature of justice):

1) Income Inequality as a Result of Rent Seeking

Certain government policies result in uneven income distribution. For an example, a paper by Patrick MacLaughlin and Lauren Stanley at the Mercatus Center empirically analyze the regressive effects of regulatory policy. Specifically, Stanley and MacLaughlin find that high barriers to entry create barriers to entry which worsens income mobility. Poorer would-be entrepreneurs cannot enter the market if they must, for an example, pay thousands of dollars for a license, or spend a large amount of time getting costly education and certifications to please some regulatory bureaucracy. This was admitted even by the Obama Administration in a recent report advising reform of occupational licensing laws. As basic public choice theory teaches, regulators are subject to regulatory capture, in which established business interests lobby regulators to erect barriers to entry to harm would-be competitors. Insofar as inequality is a result of such rent-seeking, libertarians have an obvious reason to oppose it.

Many other policies can worsen inequality. When wealthy corporations receive artificial monopolies from policies such as excessive intellectual property laws, insulating them from competition or when they gain wealth at the expense of poorer taxpayers through improper subsidies. When the government uses violent policing tactics to unequally enforce drug laws against poorer communities, or when it uses civil asset forfeiture to take the property of the worst off. When the government uses eminent domain to take the property of disadvantaged individuals and communities in the name of public works projects, or when they implement minimum wage laws that displace low-skilled workers. Or, if the structure of welfare benefits discourages income mobility, which also worsens inequality. There are a myriad of bad government policies which benefit the rich and exploit the poor, some of which are a direct result of rent-seeking on behalf of the wealthy.

If the rich are getting richer, or if the poor are stopped from becoming wealthier, as a result of government coercion, even Nozick’s entitlement theory of justice calls for us to be skeptical of the resulting income distribution. As Matt Zwolinski argues, income distributions are not only a result of, pace Nozick, a result of the free exchanges of individuals, but they are also a result of the institutions in which those individuals exchange. Insofar as inequality is a result of unjust institutions, we have good reason to call that inequality unjust.

Of course, that principle is still very hard to empirically apply. It is hard to tell how much of an unequal distribution is a function of bad institutions and how much is a function of free exchange. However, this means we can provide very limited theories of distributive justice not as constructivist attempts to mold market outcomes to our moral desires, but as rough rules of thumb. If it is true that unequal distributions are a function of bad institutions, then unequal distributions should cause us to re-evaluate those institutions.

2) Income Inequality and Government Exploitation
Of course, many with more Marxist inclinations will argue that any amount of economic inequality will inherently result in class-based exploitation. There are very good, stand-by classical liberal (and neoclassical economic) reasons to reject this as Marxian class analysis as it depends on a highly flawed labor theory of value. However, that does not mean there is not some correlation between some notion of macro-level exploitation of the worst-off and high levels of inequality which libertarians have good reason to be concerned about, for reasons closely related to rent-seeking. Those with a high amount of economic power, particularly in western democracies, are very likely to also have a strong influence over the policies set by the government. There is reason to fear that this will create a class of wealthy people who, through political rent-seeking channels discuss earlier, will control state policies and institutions to protect their interests and wealth at the expense of the worst-off in society. Using state coercion to protect oneself at the expense of others is, under any understanding of the term, coercion. In this way, income inequality can beget rent-seeking and regressive policies which lead to more income inequality which leads to more rent-seeking, leading to a vicious political-economic cycle of exploitation and increasing inequality. In fact, even early radical classical liberal economists applied theories of class analysis to this type of problem.

3) Inequality’s Impacts on Economic Growth

There is a robust amount of empirical literature suggesting that excessive income inequality can harm economic growth. How? The Economist explains:

Inequality could impair growth if those with low incomes suffer poor health and low productivity as a result, or if, as evidence suggests, the poor struggle to finance investments in education. Inequality could also threaten public confidence in growth-boosting policies like free trade, says Dani Rodrik of the Institute for Advanced Study in Princeton.

Of course, this is of special concern to consequentialist classical liberals who claim we should worry mostly about the betterment of the poor in absolute terms, since economic growth is strongly correlated with bettering living standards. There is even some reason for these classical liberals, given their stated normative reasons, to (at least in the short-term given that we have unjust institutions) support some limited redistributive policies, but only those that are implemented well and don’t worsen inequality or growth (such as a Negative Income Tax), insofar as it boosts growth and helps limited the growth of rent-seeking culture described with reasons one and two.

4) Inequality and Political Stability

There is further some evidence that income inequality increases political instability. If the poor perceive that current distributions are unjust (however wrong they may or may not be), they might have social discontent. In moderate scenarios, (as the Alsenia paper I linked to argue) this can lead to reduced investment, which aggravates third problem discussed earlier. In some scenarios, this can lead to support for populist demagogues (such as Trump or Bernie Sanders) who will implement bad policies that not only might harm the poor but also limit individual liberty in other important ways. In the most extreme scenarios (however unlikely, but still plausible), it can lead to all-out violent revolutions and warfare. At any rate, libertarians and classical liberals concerned with ensuring tranquility and freedom should be concerned if inequality increases.

5) Inequality and Social Mobility

More meritocratic-leaning libertarians might say we should be concerned about equal opportunities rather than equal outcomes. There is some evidence that the two are greatly linked. In particular, the so-called “Great Gatsby Curve,” which shows a negative relationship between economic mobility and income inequality. In other words, unequal outcomes can undermine unequal opportunities. This can be because higher inequality means unequal access to certain services, eg. Education, that can enable social mobility, or that the poorer may have fewer connections to better-paying opportunities because of their socio-economic status. Of course, there is likely some reverse causality here; institutions that limit social mobility (such as those discussed in problem one and two) can be said to worsen income mobility intergenerationally, leading to higher inequality in the future. Though teasing out the direction of causality empirically can be challenging, there is reason for concern here if one is concerned about social mobility.

The main point I’m getting at is nothing new: one need not be a radical leftist social egalitarian who thinks equal economic outcomes are necessarily the only moral outcomes to be concerned on some level with inequality. How one responds to inequality is empirically dependent on the causes of the problems, and we have some good reasons to think that more limited government is a good solution to unequal outcomes.

This is not to say inequality poses no problem for libertarians’ ideal political order: if it is the case that markets inherently beget problematic levels of inequality, as for example Thomas Piketty claims, then we might need to re-evaluate how we integrate markets. However, there is good reason to be skeptical of such claims (Thomas Piketty’s in particular are suspect). Even if we grant that markets by themselves do lead to levels of inequality that cause problems 3-5, we must not commit the Nirvana fallacy. We need to compare government’s aptitude at managing income distribution, which for well-worn public choice reasons outlined in problems one and two as well as a mammoth epistemic problem inherent in figuring out how much inequality is likely to lead to those problems, and compare it to the extent to which markets do generate those problems. It is possible (very likely, even) that even if markets are not perfect in the sense of ensuring distribution that does not have problematic political economic outcomes, the state attempting to correct these outcomes would only make things worse.

But that is a complex empirical research project which obviously can’t be solved in this short blog post, suffice it to say now that though libertarians are right to be skeptical of overarching moralistic outrage about rising levels of inequality, there are other very good empirical reasons to be concerned.

A Common Conservative Fallacy

I believe folly serves liberals better than it serves conservatives. Our way is the rational way while liberals tend to rely on their gut-feelings and on their sensitive hearts which make them comparatively indifferent to hard facts. That’s why they voted for  Pres. Obama. That’s why they voted for Mrs Bill Clinton against all strong evidence (known evidence, verifiable, not just suppositions) of her moral and intellectual unsuitability. That’s why many of them still can’t face emotionally the possibility of buyer’s remorse with respect to Mr Obama. That’s why they can’t collectively face the results of the 2016 election. So, conservatives have a special duty to wash out their brains of fallacy often.

It’s the task of every conservative to correct important errors that have found their way into fellow conservatives’ mind. Here is one I hear several times a week, especially from Rush Limbaugh (whom I otherwise like and admire). What’s below is a paraphrase, a distillation of many different but similar statements, from Limbaugh and from others I listen to and read, and from Internet comments, including many on my own Facebook:

“Government does not create jobs,”


“Government does not create wealth (it just seizes the wealth created by business and transfers it to others).”

Both statements are important and both statements are just false. It’s not difficult to show why.

First, some government actions make jobs possible that would not exist, absent those actions. Bear with me.

Suppose I have a large field of good bottom land. From this land I can easily grow a crop of corn sufficient to feed my family, and our poultry, and our pig, Gaspard. I grow a little more to make pretty good whiskey. I have no reason to grow more corn than this. I forgot to tell you: This is 1820 in eastern Ohio. Now, the government uses taxes (money taken from me and from others under threat of violence, to be sure) to dig and build  a canal that links me and others to the growing urban centers of New York and Pennsylvania. I decide to plant more corn, for sale back East. This growth in my total production works so well that I expand again. Soon, I have to hire a field hand to help me out. After a while, I have two employees.

In the  historically realistic situation I describe, would it not be absurd to declare that the government gets no credit, zero credit for the two new jobs? Sure, absent government tax-supported initiative, canals may have been built as private endeavors and with private funds. In the meantime, denying that the government contributed to the creation of two new jobs in the story above is not true to fact.

Second, it should be obvious that government provides many services, beginning with mail delivery. Also, some of the services private companies supply in this country are provided elsewhere by a branch of government. They are comparable. This fact allows for an estimation of the economic value of the relevant government services. Emergency services, ambulance service, is a case in point. Most ambulances are privately owned and operated in the US while most ambulances are government-owned and operated in France. If you have a serious car accident in the US, you or someone calls a certain number and an ambulance arrives to administer first aid and to carry you to a hospital if needed. Exactly the same thing happens in France under similar circumstances. (The only difference is that, in France, the EM guy immediately hands you a shot of good cognac. OK, it’s not true; I am kidding.)

In both countries, the value of the service so rendered is entered into the national accounting and it does in fact appear in the American Gross Domestic Product for the year (GDP) and in the French GDP, respectively. The GDP of each country thus increases by something like $500 each time an ambulance is used. Incidentally, the much decried GDP is important because it’s the most common measure of the value of our collective production. One version of GDP (“PPP”) is roughly comparable between countries. When the GDP is up by 3,5 % for a year, it makes every American who knows it, happy; also some who don’t know it. When the GDP shrinks by 1%, we all worry and we all feel poorer. If the GDP change shrinks below zero for two consecutive quarters, you have the conventional definition of a recession and all hell breaks lose, including usually a rise in unemployment.

Exactly the same is true in France. The government-provided French ambulance service has exactly the same effect on the French GDP.

Now think of this: Is there anyone who believes that the equivalent service supplied in France by a government agency does not have more or less the same value as the American service provided by a private company? Would anyone argue that the ambulance service supplied in France, in most ways identical to the service in America, should not be counted in the French GDP? Clearly, both propositions are absurd.

Same thing for job creation. When the French government agency in charge of ambulances hires an additional ambulance driver, it creates a new job, same as when an American company hires an ambulance driver.

By the way, don’t think my story trivial. “Services” is a poorly defined category. It’s even sometimes too heterogeneous to be useful (not “erogenous,” please pay attention). It includes such disparate things as waitressing, fortune-telling, university teaching, and doing whatever Social Security employees do. Yet it’s good enough for gross purposes. Depending on what you include, last year “services” accounted for something between 45% and 70% of US GDP. So, if you think services, such as ambulance service, should not be counted, you should know that it means that we are earning collectively about half to three quarters less than we think we do. If memory serves, that means that our standard of living today is about the same as it was in 1950 or even in 1930.

Does this all imply that we should rejoice every time the government expands? The answer is “No,” for three reasons. These three reasons however should only show up after we have resolved the issue described above, after we have convinced ourselves that government does provide service and that it and does create real jobs, directly and indirectly. Below are the three questions that correspond to the three reasons why conservatives should still not rejoice when government enlarges its scope. Conservatives should ask these three questions over and over again:

1 Is this service a real service to regular people or is it created only, or largely, to serve the needs of those who provide it, or for frivolous reasons? Some government services fall into this area, not many, I think. Look in the direction of government control, inspection, verification functions. Don’t forget your local government.

Often, the answer to this question is not clear or it is changing. Public primary and secondary education looks more and more like a service provided largely or even primarily to give careers to teachers and administrators protected by powerful unions. It does not mean that the real, or the expected service, “education,” is not delivered, just that it’s often done badly by people who are not the best they could be to provide that particular service; also people who are difficult or impossible to replace.

2   Is this particular service better provided by government or by the private sector? Is it better provided by government although the provision of the service requires collecting taxes and then paying out the proceeds to the actual civil servants through a government bureaucracy? That’s a very indirect way to go about anything, it would seem. That’s enough reason to be skeptical. The indirectness of the route between collecting the necessary funds and their being paid out to providers should often be enough to make government service more expensive than private, market-driven equivalent services. Note that the statement is credible even if every government employee involved is a model of efficiency.

The US Post Office remains the best example of a  situation where one would say  the private sector can do it better.

Only conservatives dare pose this question with respect to services one level of government or other has been supplying for a long time or forever. The Post Office is inefficient; if it were abolished, the paper mail would be delivered, faster or cheaper, or both. Some paper mail would not be delivered anymore. Many more of us would count it a blessing than the reverse. While there is a broad consensus across the political spectrum that children should be educated at collective expense, there is growing certitude that governments should not be in the business of education. In many parts of the country, the public schools are both expensive and bad. Last time I looked, Washington DC was spending over $20,000 per pupil per year. Give me half that amount and half the students or better will come out knowing how to read, I say. (It’s not the case now.)

3   This is the most serious question and the most difficult to answer concretely: Does the fact that this service is provided by government (any level) have any negative effect on our liberties? This is a separate question altogether. It may be that the government’s supply of a particular service is both inefficient and dangerous to freedom. It may be however that government supply is the most efficient solution possible and yet, I don’t want it because it threatens my freedom. As a conservative, I believe that my money is my money. I am free to use it to buy inefficiently, in order to preserve liberty, for example. I am not intellectually obligated to be “pragmatic” and short sighted.

To take an example at random, if someone showed me, demonstrated beyond a reasonable doubt, that Obamacare would reduce the cost of health care without impairing its quality, if that happened, I would still be against it because of the answer I would give to the third and last question above.

I don’t want a any government bureaucracy to make decisions that are ultimately decisions of life and death on my behalf. The possibility of blackmail is too real. Even thinking about it is likely to make some citizens more docile than they otherwise would be. So much power about such real issues must have a chilling effect on the many.

The rule of thumb is this: Every expansion of government reduces individual freedom. That’s true even if this expansion creates and efficient and effective government agency, say, a real good Post Office we don’t even know how to dream of. And this is not an abstract view. The well-intentioned and in other ways laudable recognition of homosexual marriage was followed in short order by threats and fines against a hapless baker who declined to bake a cake for a gay wedding. We must keep in mind at all times that, of course, the power to fine, like the power to tax, is the power to destroy.

An efficient but ethically objectionable government service is not something I worry much about, in the case of Obamacare specifically, by the way. It is inefficient, ineffective and dangerous to individual liberty all at once.

Conservatives don’t do enough to proclaim that their opposition to big government has an ethical basis, that it’s a moral position independent of the quality of big government. This silence makes if easy for liberals to caricature conservatives as just selfish grouches who don’t want to pay taxes.

Most of the time, I don’t want to pay taxes because I don’t want to be forced. I would gladly give away twice the amount of my taxes if there were a way to do it voluntarily instead of paying taxes.

I am so opposed to this kind of force that I think even the undeserving and obscenely rich should not be despoiled by the government. It’s an ethical position, not a pragmatic one. And, it sure cannot be called “selfish.” (WTF!)

On 19th Century Tariffs & Growth

A few days ago, Pseudoerasmus published a blog piece on Bairoch’s argument that in the 19th century, the countries that had high tariffs also had fast growth.  It is a good piece that summarizes the litterature very well. However, there are some points that Pseudoerasmus eschews that are crucial to assessing the proper role of tariffs on growth. Most of these issues are related to data quality, but one may be the result of poor specification bias. For most of my comments, I will concentrate on Canada. This is because I know Canada best and that it features prominently in the literature for the 19th century as a case where protection did lead to growth. I am unconvinced for many reasons which will be seen below.

Data Quality

Here I will refrain my comments to the Canadian data which I know best. Of all the countries with available income data for the late 19th century, Canada is one of those with the richest data (alongside the UK, US and Australia). This is largely thanks to the work of M.C. Urquhart who recreated the Canadian GNP series fom 1870 to 1926 in collaborative effort with scholars like Marvin McInnis, Frank Lewis, Marion Steele and others.

However, even that data has flaws. For example, me and Michael Hinton have recomputed the GDP deflator to account for the fact that its consumption prices component did not include clothing. Since clothing prices behaved differently than the other prices from 1870 to 1885, this changes the level and trend of Canadian incomes per capita (this paper will be completed this winter, Michael is putting the finishing touch and its his baby).  However, like Morris Altman, our corrections indicate a faster rate of growth for Canada from 1870 to 1913, but in a different manner. For example, there is more growth than believed in the 1870-1879 period (before the introduction of the National Policy which increased protection) and more growth in the 1890-1913 period (the period of the wheat boom and of easing of trade restrictions).

Moreover, the work of Marrilyn Gerriets, Alex Chernoff, Kris Inwood and Jim Irwin (here, here, here, here) that we have a poor image of output in the Atlantic region – the region that would have been adversely affected by protectionism. Basically, the belief is a proper accounting of incomes in the Atlantic provinces would show lower levels and trends that would – at the national aggregated level – alter the pattern of growth.

I also believe that, for Quebec, there are metrological issues in the reporting of agricultural output. The French-Canadians tended to report volume units in manners poorly understood by enumerators but that these units were larger than the Non-French units. However, as time passed, census enumerators caught on and got the measures and corrections right. However, that means that agricultural output from French-Canadians was higher than reported in the earlier census but that it was more accurate in the later censuses. This error will lead to estimating more growth than what actually took place. (I have a paper on this issue that was given a revise and resubmit from Agricultural History). 

Take all of these measurements issue and you have enough doubt in the data underlying the methods that one should feel the need to be careful. In fact, if the sum of these (overall) minor flaws is sufficient to warrant caution, what does it say about Italian, Spanish, Portugese, French, Belgian, Irish or German GDP ( I am not saying they are bad, I am saying that I find Canada’s series to be better in relative terms).

How to measure protection?

The second issue is how to measure protection. Clemens and Williamson offered a measure of import duties revenue over imports volume. That is a shortcut that can be used when it is hard to measure effective protection. But, it may be a dangerous shortcut depending on the structure of protection.

Imagine that I set an import duty so high as to eliminate all entry of the good taxed (like Canada’s 300% import tax on butter today). At that level, there is zero revenue from butter import and zero imports of butter. Thus, the ratio of protection is … zero. But in reality, its a very restrictive regime that is not being measured.

More recent estimates for Canada produced by Ian Keay and Eugene Beaulieu (in separate papers, but Keay’s paper was a conference paper) attempted to measure more accurate indicators of protection and the burden imposed on Canadians. Beaulieu and his co-author found that using a better measure, Canada’s trade policy was 11% more restrictive than believed. Moreover, they found that the welfare loss kept increasing from 1870 to 1890 – reaching a figure equal to roughly 1.5% of GDP (a non-negligible social cost).

It ought to be noted though that alongside Lewis and Harris, Keay has found that the infant industry argument seems to apply to Canada (I am not convinced, notably for the reasons above regarding GDP measurements). However, that was in the case of Canada only and it could have been a simple outlier. Would the argument hold if better trade restriction measures were gathered for all other countries, thus making Canada into a weird exception?

James Buchanan to the rescue

My last argument is about political economy. Was the institutional arrangement of protection a way to curtail government growth? Protection is both a method for helping national industries and for raising revenues. However, the government cannot overprotect at the risk of loosing revenues. It must protect just enough to allow goods to continue entering to earn revenues from imports.  This tension is crucial especially since most 19th century countries did not have uniform general tariffs (like a flat 5% import duty) which would have very wide bases. The duties tended to concern a few goods very heavily relative to other goods. This means very narrow tax bases.

Standard public finance theory mandates wide tax bases with a focus on inelastic sources. However, someone with a public choice perspective (like James Buchanan) will argue that this offers the possibility for the government to grow. Basically, a public choice theorist will argue that the standard public finance viewpoint is that the sheep is tame. Self-interested politicians will exploit this tameness to be elected and this might imply growing government. However, with a narrow and elastic tax base, politicians are heavily constrained. In such a case, governments cannot grow as much.

The protection of the 19th century – identified by many as a source of growth – may thus simply be the symptom of an institutionnal arrangement that was meant to keep governments small. This may have stimulated growth by keeping other sectors of the economy more or less free of government meddling. So, maybe protection was the offspring of the least flawed institutional arrangement that could be adopted given the political economy of the time.

This last argument is the one that I find the most convincing in rebuttal to the Bairoch argument. It means that we are suffering from a poor specification bias: we have identified a symptom of something else as the cause of growth.

Spanish GDP since 1850

Among the great economic historians is Leandro Prados de la Escosura. Why? Because, before venturing in massively complex explanations to explain academic puzzles, he tries to make sure the data is actually geared towards actually testing the theory. That attracts my respect (probably because it’s what I do as well which implies a confirmation bias on my part). Its also why I feel that I must share his most recent work which is basically a recalculation of the GDP of Spain.

The most important I see from his work is that the recomputation portrays Spain as a less poor place than we have been led to believe – throughout the era. To show how much, I recomputed the Maddison data for Spain and compared it with incomes for the United Kingdom and compared it Leandro’s estimates for Spain relative to those for Britain (the two methods are very similar thus they seem like mirrors at different levels). The figure below emerges (on a log scale for the ratio in percentage points). As one can see, Spain is much closer to Britain than we are led to believe throughout the 19th century and the early 20th century. Moreover, with Leandro’s corrections, Spain convergence towards Britain from the end of the Civil War to today is very impressive.


The only depressing thing I see from Leandro’s work is that Spain’s productivity (GDP / hours worked) seems to have stagnated since the mid-1980s.


On getting the data right : price disparities before 1914

I am a weird bird. I get excited at weird things. I get excited at reading economics and history papers (and books). I get particularly excited when I read papers and books that “get the data right”. This is because I believe that most theoretical debates in economics stem from poor data forcing us to develop grandiose theories or very advanced models to explain simple things. One example of that is the work of Joshua Hendrickson who argued that monetary aggregates (M1, M2 etc.) are not necessarily perfect indicators of money. However, these aggregates were used in statistical tests and generated strange results inconsistent with theory. This issue has been the cause of many debates. Josh stepped in and said that we just had a variable that was not created to measure what the theory said. Using broader measures of money, he found the results consistent with theory. The debates were driven by poor data (as I think is the case in issues over fiscal multipliers, crowding-out and business cycles).

Thus, I am always excited to see data work that “get things right”. One recent example that adds to cases like that of Hendrickson is Peter Lindert’s working paper at the National Bureau of Economic Researcher. Now, before I proceed, I must state that I am very partial to Lindert as he has been a big supporter of my own research and has volunteered important quantities of his time to helping me move forward. Thus, I have a favorable bias towards Lindert (and his partner in crime, Jeffrey Williamson).  Nonetheless, his working paper requires a discussion because it “gets prices right”.

The essence of his new working paper is that our GDP per capita estimates prior to 1914 may overestimate divergence between countries over time.

Generally, when we measure GDP, we try to derive “volume indexes” that measure quantities produced at a fixed vector of prices. For example, when I measured Canadian economic growth from 1688 to 1790 (I am submitting it in a few weeks), I took the quantities of grain reported in censuses and weighed them by prices for a fixed year. This is a good approach for measuring productivity (changes in quantities). Nonetheless, there are issues when you try to move this method over a very long period in time. The prices may become unrepresentative.  So you get time-related distortions. Add to this that all the time-related distortions may be different over space. After all, should we believe the relative price of wheat to oats in 1910 was the same in Canada as it was in Russia?  Variations in relative prices over space will affect this issue. Basically, you juxtapose these two types of distortions when trying to measure GDP per capita over centuries and you may end up so far in the left field that you’re in fact in the right field.

In his working paper, Lindert tried to adjust for those problems by moving to prices that were more representative. The approach he used is basically the one used by Robert Allen in his work on the Great Divergence. You create a bundle of goods that capture the cost of living in different regions – a basic bundle of goods. This generates purchasing power parities. From there, he recomputed incomes per capita with these measures prior to 1914. The results are striking: there is much more divergence between Europe and Asia that commonly proposed and the United States are much richer than otherwise believed (and were more richer very early on – as far back as the colonial era).

Now, why does this matter?

Well, consider the debate on convergence. Many scholars have been unimpressed by the level of income convergence across countries (at least until the 1980s). However, Lindert’s estimates suggest that the starting point was well below what we think it was. In a way, what this is telling us is that many puzzles regarding the “catching-up” of poor countries may be simply related to poor data. Imagine, for a second, that we could redo what Lindert did with many more countries at a higher time frequency. What would this tell us? Imagine also that this new data would confirm Lindert’s point, what would that entail for those entangled in debates over development?

Basically, what I am saying is this: most of our debates often stem from poor data. If a simple (and theoretically sound) correction can eliminate the puzzles, maybe our task as economists should be to stop bickering over advanced theory and make sure the data is actually geared towards testing our theories!

Has Canada been Poorer than the US for so long?

A standard stylized fact in Canada is that we are poorer, on average, than the average American. This has been presented as a fact that is as steady as the northern star. But our evidence on Canadian incomes is pretty shoddy prior to 1870 (here I praise M.C. Urquhart for having designed a GNP series that covers from 1870 to 1926 and links up with the official national accounts even if I think there are some improvements that can be brought to measuring output from some key industries and get the deflator right). But what about anything before 1870? There are some estimates for Ontario from 1826 to 1851 by Lewis and Urquhart (great stuff), but Ontario was pretty much the high-income of Canada.

So, can we go further back? This is what my work is about (partially), and I just made available my results on Canadian living standards (proxied by Quebec where the vast majority of the population was) from 1688 to 1775 as captured by welfare ratios. So that’s pretty much the closest we can get to the “founding”. Below are my results derived from this paper. They show that the colonists in Canada were not very much richer than their counterparts in France with the basket meant to capture the meanest of subsistence and roughly equal to their counterparts in France with a basket that includes more manufactured goods like clothing and more alcohol. This explains why most migrants from France to Canada were “volunteered” (in the sense that they were pretty much reluctant migrants) for migration. But the key interesting result is that relative to New England – the poorest of the American colonies – it is poorer regardless of the basket used. Thus, there seems to be truth to the common logic about Canadians being always poorer than the Americans.


However, I am not fully convinced of my own results. This may surprise some. The reason is not that I do not trust my data (in fact, I think it is superior to most of what exists for the time given that I will be able to proceed to tons of other data). The reason is simple (and rarely discussed): natives.

Natives are always omitted from the stories of living standards. But they existed nonetheless. In terms of national accounts, if the British and French settlers dispossessed and killed natives, their welfare losses are just not computed. But the welfare losses of a musket shot to the head are real. I have always been convinced that if we could correct estimates of living standards to account for the living standards of natives, the picture would change terribly. The reason is two-fold. The first reason is that the historiography is pretty clear that while they were obviously not nice, the French were nicer than the British towards the Natives (at least until 1763 when the British shifted strategy). In fact, trade between French and Natives was very frequent and so it might be that for the whole population (natives + settlers), the French-area peoples enjoyed more growth and higher average levels. In the British colony, if the settlers killed and dispossessed natives, this is basically the British turning native capital stocks into their own capital stock or into consumption (which would enter settlers GDP but not change total GDP). In essence, this is basically a variation on the arguments of Robert Higgs with regards to measuring the American GDP in World War Two and Albrecht Ritschl on the German interwar growth. I am pretty sure that adjusting for the lives of natives would show a greater level for Canada leading to rough equality between the two colonies. However, I am not sure if the argument would cut that way (my guts say yes) since in their conjectural growth estimates, Mancall and Weiss show that with the natives included, their zero rate of income per capita growth turns into a positive rate.

Nonetheless, I still think that knowing that the settlers were better off in the US as an improvement over the current state of knowledge. Until ways to impute the value of native output and production are found, my current estimates are only a step forward, not the whole nine yards.

Minimum wage, measurements and incarceration rates

A few weeks ago, I published a blog post about how incarceration rates affect our measurement of the relative economic conditions of Blacks in America. My claim was that the statistics are hiding a reversal of the painfully achieved advances secured between 1870 and 1960. Basically, my claim was that those who (in greater numbers) found their ways to a prison cell tended to be at the bottom of the income distribution, were more susceptible to be unemployed and had lower wages. This creates a composition effect whereby the official surveys cream-skim the top of black wage, income and employment distributions.

But, could this problem also affect our measurement of the effects of minimum wage? Let me be clear before you continue ahead, I am just asking this question because I could find no satisfactory answer to (or even mention of) this issue.

In recent times, minimum wage surveys have tended to find some gains in earnings for some workers following increases in minimum wage rates. Regardless of how you look at the prison population, it increases  – albeit at a decelerating rate since the early 2000s – since the 1980s. Coincidentally, that starting point is also the point at which the famous Minimum Wage Study Commission was published (1981). That report basically cemented the point made by George Stigler (i.e. minimum wages are not desirable). That report surveyed the entire literature to summarize the amplitude of the effects. That literature encompassed articles written between the end of the Second World War and … well… 1981. If you look below at the graph, incarceration rates were more or less constant during that regime. Thus, if there were composition effects associated with surveys of wages, incomes and employment, they were more moderate than after 1981 when incarceration rates surged.


But, its also after 1981 that some papers began to find some positive effects of minimum wage increases. These studies took place under a growing composition problem in surveys of wages, incomes and employment. Take the famous Dube, Lester and Reich paper in the Review of Economics and Statistics  who used data from 1990 to 2006. During that period, the male incarceration rate surged from 297 per 100,000 to 501 per 100,000. I understand that DLR used a time fixed effect method, but would that be sufficient to at least deal with the issue of shifting labour supplied (it won’t for the data bias issues described notably by Bruce Western)

If we assume that those who are plausibly affected by minimum wages (i.e. lower income individuals) are also those more likely to end up in jail in the United States, then there is clearly a bias. As they are dropped from the labor market (or as they join the prison population), they leave only the workers least affected by the minimum wage inside the samples. That is one possibility.

The other possibility – which is that surveys do not suffer from a large composition, but which is not mutually exclusive to that composition problem – is that the growing prison population represents a year-over-year reduction in the labor supply which offsets the effects of hikes in the minimum wage (or maybe even eliminates them entirely if the shift is big enough).


I have tried many variations of this google scholar research and went back to my copy of the Handbook of Labor Economics and my Economics of Inequality, Poverty and Discrimination  (a book worth reading by the way) and I found very little on this point. Very few scholars have considered the possibility of this problem (which implies a shift of the labor supply curve concurrent with minimum wage hikes and a composition problem where those affected are simply not measured anymore). Yet, I feel like this is a defensible claim. In England, where some studies also show minimal effects or positive effects of the minimum wage, there has also been an increase in the prison population. In contrast, Canada – whose prison population is declining moderately (meaning that the labor supply is increasing as the minimum wage is being increased – the studies do tend to find the “conventional” result.

Am I crazy or is this a case of poor measurement? Personally, I feel that there must an answer, but please tell me I did not just stumble on this!

Sensitive and Crucial: on Measuring Living Standards in the 18th Century

In the course of the twitterminar on the High-Wage Economy argument (HWE) which generated responses from John Styles on his blog (who has convinced me that the key solution to HWE rests in Normandy, not the Alsace) and many other on Twitter. In the course of that discussion, I skirted a point I have been meaning to make for a long time. However, I decided to avoid it because it is tangentially related to the HWE story. Its about how we measure living standards over space in the past.

Basically, the HWE story is a productivity story and all that matters in such a story is wage rates relative to other input prices. Because we’re talking about relatives, the importance of proper deflators is not that crucial. However, when you move beyond HWE and try to ask the question regarding absolute differences over space in living standards, the wage rates are not sufficient and proper deflators are needed.

They are many key issues to estimating living standards across space. The largest is that given that very few goods crossed borders in the past, converting American incomes into British sterling units using reported exchange rates would be rife with errors and calculating purchasing power parities would be complicated. The solution, very simple and elegant by its simplicity, is to rely on the logic of the poverty measures. Regardless of where you are, there is a poverty threshold. Then, all that is needed is to express incomes as the ratio of income to the poverty line. If the figure is three, then the average income buys three times the poverty line. Expressed as such, comparisons are easy to do. This is what Robert Allen did and it was basically a deeper and more complete approach than Fernand Braudel’s “Grain-Wages” (wage rates divided by grain prices).

Where should the line be?

While this represents a substantial improvement for economic historians like me who are deeply interested in “getting the data right”, there are flaws. In the course of my dissertation on living standards in Canada (see also my working papers here and here), I saw one such flaw in the form of how long the length of the work year was. In fact, a lot of my comments in this post were learned on the basis of Canada as an extreme outlier in terms of sensitivity. In Canada, winter is basically a huge preindustrial limitation on the ability to work year-round (thus, the expression mon pays ce n’est pas un pays, c’est l’hiver). But this flaw is only the tip of the iceberg. First of all, the winter means that the daily energy intake must substantially greater than 2,500 calories in order to maintain body mass. The mechanism through which the temperature increases the energy requirements of the human metabolism is in part the greater weight carried by the heavier clothing in addition to the energy needed by the body to maintain body temperature. At higher altitudes, these are compounded by the difference in air pressure.In their attempt to construct estimates of the living standards of Natives in the Canadian north during the fur trade era, Ann Carlos and Frank  Lewis assert that it is necessary to adjust the basket of comparison to include more calories for the natives given the climate – they assert that 3500 calories were needed rather 2500 calories for English workers.In Russia, Boris Mironov estimated that the average calories ingested stood at 2952 per day between 1865 and 1915 while the adult male had to consume 3204 calories per day. In Canada in the 18th century, it was estimated that patients at the Augustines hospital in Quebec City required somewhere 2628 calories and 3504 calories per day while soldiers consumed on average 2958 calories per day and the average population consumed 2845 calories per day (see my papers linked up above).  The range of calorie requirements for soldiers (which I took from a reference inside my little sister’s military stuff) is quite large: from 3,100 in the desert at 33 degrees Celsius to 4,900 in artic conditions (minus 34 degrees Celsius) – a 58% difference. So basically, when we create welfare ratios for someone in, say, Mexico, the calories needed in the basket should be lower than in the Canadian basket.

Another issue, of greater importance, is the role of fuel. In the welfare ratios commonly used, fuel is alloted at 2MBTU for the basic level of sustenance which. This is woefully insufficient even in moderately warm countries, let alone Canada. My estimates of fuel consumption in Canada is that the worst case hovers around 20MBTU (ten times above the assumption) if the most inefficient form of combustion (important losses) and the worst kind of wood possible (red pine). Similar levels are observed for the American colonies.

Combined together, these corrections suggest that the Canadian poverty threshold should be higher than the one observed in France, England, South Carolina or Argentina. These adjustments can more or less be easily made by using military manuals. The army measures the basic calories requirements for all types of military theaters.

How to factor in family size and use equivalence scales. 

Equivalence scales refer to the role of family size. Given the same income, families of different size will have different levels of welfare. Thanks to economies of scale in housing, cooking, lighting and heating, larger households can get more utility out of one dollar of income. That adjustments are required to render different households comparable is well accepted amongst economists. However, given the sensitivity of any analysis to the assumptions underlying any adjustments, there is an important debate to be had.

The convention among economic historians has been to assume that households have three adult equivalents. This assumption has gone largely undiscussed. The problem is “which scale to use”. The conversion into adult equivalents is subject to debates. Broadly speaking, three approaches exist. The first uses the square root of the number of individuals. The second attributes the full weight of the first adult, half the weight of the second adult and 30% for each child. This approach is commonly used by the OECD, Statistics Canada and numerous government agencies in Canada The third approach is the one used by the National Academy of Sciences in the United States which proposed to use an exponent ranging between 0.65 and 0.75 to household size but only after having multiplied the number of children by 0.7. As a result, a family of four (two parents, two infants) can have either 2 adult equivalents (square root), 2.1 adult equivalents (OECD and Statistics Canada approach) or 2.36 adult equivalent (NAS approach). The differences relative to the square roots approach are 5% and 18%. If we move to a family of 6 persons, the differences increase to 10.22% and 34.72%.  If we are comparing regions with identical family structures, this would not be a problem. If not, then it is an issue. The selection of one method over another would have important effect on the cost of the living basket, with the NAS approach showing the costliest basket. Using a method relatively close to that of the OECD (although not exactly that measure), Eric Schneider found that the relatively small size of families in England led Allen to underestimate living standards. In a more recent paper, Allen alongside Schneider and Murphy pointed out that extending Schneider’s analysis to Latin America where “family sizes were likely larger (…) than in England and British North America” would amplify the wage gap between the two regions.


The table above shows how much family size varied around the late 17th century across region. Clearly, this is a non-negligible issue.

Sensitivity of estimates

Just to see how much these points matter, let’s modify for two easily modifiable factors: household size (given the numbers above) and fuel requirements (calories from food are harder to adjust for and I am still in the process of doing that). Let’s recompute the welfare ratios (those classified as bare bones) of Canada (the outlier) relative to the other according to different changes circa the end of the 17th century. How much does it matter?

Comparing New World places like Canada and Boston does not change much – they are more or less similar (family size and relative price-wise). However, just adjusting for family size eliminates a quarter of the gap between Canada and Paris (from 61% to somewhere 43.9% and 49.5%). Then, the adjustment for the fact that it is freezing cold in Canada eliminates a little more than half the advantage Canada enjoyed. So roughly two third of the Canadian advantage over Paris (the richest place in France) is eliminated by adjusting for family size and fuel consumption without adjusting for food requirements. However, family size does not affect dramatically the comparison between Paris and London (regardless of whether we use the Allen figures or the Stephenson-Adjusted figures).  Thus, most of the sensitivity issues are related to comparing the New World with the Old World. effectofcorrections

Still, there are some appreciable differences from family structures within Europe (i.e. the Old World) that may alter the relative positions.  For example, Ireland had much larger families than England in the 18th century (see here – the authors shared their dataset with me and a co-author): in 1700, England & Wales had an average household size of 4.7 compared with 5.32 in Ireland. That would moderately disrupt the comparison. Not as much as comparison between the New World and Old World, but enough to make cautious about European differences.


I have seen many discussions regarding the sensitivity of welfare ratios in numerous papers. I am not attempting to make my present point into some form of revolutionary issue. However, all the sensitivity estimates were concentrated on a case or another and they all concern a specific problem. No one has gathered all the problems in one place and provided a “range of estimates”. Maybe its time to go in that direction so that we know which place was poor and which was not (relative to one another, since anything preindustrial was basically dirt-poor by our modern standards).

On Capitalism and Slavery : Pêle-Mêle Comments

Last week, a debate was initiated via an article in the Chronicle of Higher Education that relates to the clash between historians and economists over the topic of slavery. The debate seems acrimonious given the article and at the reading of a special issue of the Journal of Economic History regarding the Half has never been told by Edward Baptist, its hard to conclude otherwise. Pseudoerasmus published comments on the issue in a series of posts and a Trumpian twitterstorm (although the quality is far from being Trumpian). I find myself largely in agreement with him in response to the historians, but there are some pêle-mêle points that I felt I needed to add.

On Historians Versus Economists

To be honest, when I took my first classes in economic history, it seemed clear that there were important points that were agreed upon in the literature on slavery. The first was that the accounting profitability of slavery was not the same as the economic profitability (think opportunity cost here) of slavery. Thus, it was possible that (concentrating on the US here) the peculiar institution could more or less thrive regardless of the social costs it imposed (i.e. slavery is a tax on leisure which also increases the expropriation rate from slaves, and non-slaveowners often had to shoulder the cost of enforcing the institution). This argument is not at all new; in fact it is basically a public choice argument that Gordon Tullock and Anne Krueger could have signed on to without skipping a heartbeat (see Sheilagh Ogilvie – one of my favorite economist who does history in equality with Jane Humphries). The second point of agreement is that no one agreed on how to measure the productivity of slavery in the United States and the distribution of its costs and gains. The second point has been a very deep methodological debate which related to the method of measuring productivity (CES vs Translog TFP – stuff that would make your head blow and which also lead to the self-invitation of the Cambridge Capital Controversy to the debate). The quality of the data has been at the centre-stage as well, and datasets on slave prices, attributes, tasks and many other variables are still being collected (see notably the breathtaking work of Rhode and Olmsted here and here).

Thus, I will admit to being unimpressed by the use of oral histories to contest that literature. In addition, the absence of theory in Baptist’s work yields an underwhelming argument. Oral histories are super-duper important. The work of Jane Humphries on child labor is a case in point of the need to use oral histories. She very carefully used the tales told by children who worked during the industrial revolution to document how labor markets for children worked. The story she told was nuanced, carefully argued and supported by other primary evidence. This is economic history at its best – a merger of cliometrician and historian. In fact, while this is an evaluation that is subjective, the best economists are also historians and vice-versa. The reason for that is the mix of theory with multiple forms of evidence. But they key is to have a theory to guide the analysis.

Unexpectedly for some, the best exposition of this argument comes from Ludwig von Mises in his unknown book Theory and HistoryI was made aware of that book in a discussion with Chris Coyne of George Mason University and I proceeded to reading it. I was surprised how many similarities there were between the Mises who wrote that book and the Douglass Norths and the Robert Fogels of this world. The core argument of Theory and History is that axiomatic statements can be applied to historical events. The goal of historians and economic historians is to sort which theory applies. For example, the theory of signaling and the theory of asymmetric information are both axiomatically true. Without the need for evidence, we know that they must exist. The question of an economic historian becomes to ask “did it matter”? Both theories can compete to offset each other: if signaling is cheap, then asymmetric information can be solved; if it is not, asymmetric information is a problem. Or both may be irrelevant to a given historical development. To explain which two axiomatic statements apply to the event (and in what dosage), you need data (quantitative and qualitative). Thus, Theory and History actually proposes the use of econometrics and statistical methods because it does not try to predict as much as it tries to a) sort which axiomatic statements applied; b) the relative strengths of competing forces; c) the counterfactual scenario.

Without theory, all you have is Baptist’s descriptions which tell us very little and can, incidentally, be distorted by he who recounts the tales he read.

On the Culture of Peasants/Slaves/Slaveowners

When I started my PhD dissertation Canadian economic history, the most annoying thing I saw was the claim that the French-Canadians had “different mentalities” or “more conservative outlooks”. This was basically the way of calling them stupid. This has recently evolved to say that they “maximized goals other than wealth”. Regardless, this was basically: the French-Canadian was not culturally suited for economic development.

But culture is not a fixed variable, it is not an exogenous variable. Culture is basically the coherent framework built by individuals who share certain features to “cut out” the noise. Everyday, we are bombarded with tons of pieces of information and there is no way that the human brain can process them all. Thus, we have a framework – culture (ideology does the same thing) – which tells us what is relevant and what is irrelevant and what interpretation to give to relevant information.

People can cling to old beliefs for a long time, but only if there is no cost to them. I can persist in terrible farming practices if I am not made aware of the proper valuation of the opportunity I am foregoing. For example, British farmers who arrived in Quebec in the 19th century tended to use oxen as they did in England for tilling the soil. They had probably been taught to do that by their parents who learnt it from their grandparents because it was part of the farming culture of England. The behavior was culturally inherited. However, when they saw that the French-Canadians were using horses and that horses – in the Canadian hinterland – got the job done better, they shifted. The culture changed at the sight of how important was the foregone opportunity by continuing to use oxen. Where the British and the French co-existed, both were equally good farmers. Where they could not observe each other, they were all sub-optimal farmers. Seeing the other methods forced changes in culture.

The same applies to slaveowners and slaves! Slaveowners were a more or less tightly knit group that frequented similar circles and were constantly on the lookout to increase productivity. If some master had noticed that he could increase production by whipping more slaves, why would he not adopt this method? Why would he leave 100$ bill on the street? Why did the masters growing cotton in South Carolina not adopt the method of whipping adopted by growers in Louisiana? Without a theory of how culture changes (and what purposes it serves beyond the simplistic Marxist power structure argument), there is no answer to this question. With the work of Rhode and Olmstead, there is an answer: the type of cotton that had higher yields was not suited for growing everywhere! In this case, we are applying my comment from the section above on Historians versus Economists. There are competing theories of explaining increasing output: either some slave masters were unable to observe the other slave masters and adopt the torture methods they had (which would need to be the case for Baptist to be right) or there were biological limitations to growing the better crops in some areas (Rhode and Olmstead).

Two competing theories (they are not mutually exclusive though) that can be tested with data and they set a counterfactual. That is why you need theory to make good history.

One last thing: slave owners were not capitalists

This is probably the most childish thing to come out of works like those of Baptist: to assert that because slaves were capital assets, that the owners were capitalists. That is true if you want to adhere to the inconsistent (and self-contradicting) Marxist approach to capital. In fact, as Phil Magness pointed out to me, slave owners were not free market types. They were very much anti-capitalists. Slavery apologists like Fitzhugh and Carlyle were even more anti-capitalists than that. It’s not because you own capital that you are a capitalist unless you adhere to Marxist theory.

But, capital is just a production input. Its value depends on what it can produce. As Jeffrey Hummel pointed out, there is a deadweight loss from slavery: enforcement costs, the overproduction of cotton because slavery is basically a tax on leisure and the implicit taxation of the output produced by slaves. All three of these factors would have slowed down economic growth in the south. Thus, as capital assets, slaves were relatively inefficient.

The most depressing thing with Chetty et al.

The Chetty et al. paper has been on my mind over the weekend (see Saturday’s post). The one thing that has moved more or less in line with the absolute mobility measure of Chetty et al. has been…the size of government.

I know that as soon as some of you read the last four words on the previous paragraphs, your eyes rolled. However, even from a social-democratic perspective, it is depressing! It is not the first time I make this observation.   In the pages of Essays in Economic and Business HistoryI recently reviewed Unequal Gains (authored by Peter Lindert and Jeffrey Williamson and published at Princeton University Press) and I observed that the “great leveling” they observed from the 1910s to the 1970s had a lot to do with the northward migration of American blacks, the closing of the gender wage gap and the convergence of the southern states. I also observed that the increase in inequality in the United States after 1970 occurred at the same time as an the state grew more in size and scope (see blog post here).

However, as I mentioned elsewhere, I am very skeptical of the tax-based data on inequality in the United States and I am afraid to push that point. However, the Chetty et al. data provides further confirmation: trends in inequality/social mobility deteriorates as the state becomes more active (see the graph below).


Now, I am aware that the causality can cut both ways. It may be that inequality (economic mobility) is rising (falling) in spite of increasing state action, it may be that state action is fueling the the rise (reduction) of inequality (economic mobility) or it may be that the state has no effects whatsoever on the evolution. Regardless of which of the three viewpoints you tend to adopt (I lean towards a mixture the second option – see my paper with Steve Horwitz here which is under consideration for publication), the implications are immensely depressing with regards to social policy in the last 75 years.

Sons outearning Fathers in Chetty et al. : working hours should be considered

In response to my post yesterday, my friend and economist/nuclear engineer (great mix) Laurent Béland pointed out that the Father-Sons mobility figures in Chetty et al. are depressing. Yes, at first glance, they are (see below – the red line). fathersons

But, at second glance, it is not as terrible. Think about family structures with the 1940 birth cohorts. The father works and, in most likelihood, the mother is a stay-at-home father. Most of the earnings come from the father who probably works 45 to 60 hours a week.  If my father earns 40,000$ at 60 hours a week or earn 40,000$ at 40 hours a week, the line remains at the same height, but we are not talking about the same living standard in reality. Chetty et al. do not account for hours worked to achieve income.  The steep decline – faster than the baseline of household-size adjusted decline – matches the steep increase in female labor force participation and the decline labor force participation of males (see graph here and Nicolas Eberstadt’s work here) as well as the decline in hours worked by males.

If the question had been “what are your chances of out-earning your father per hour worked”, then the red line would not have fallen like that. Income divided by labor supplied would probably bring the red-line back with the blue-line.

Note: Again, please note that I am not trying to rip apart Chetty et al. (as some have claimed elsewhere). Their work is great and as a guy who does all his research on providing data series regarding economic history, I am never going to rip on someone who does hard data work like Chetty et al. did ! My point is that I am not convinced that the decline is so big. And, in good faith, it seems that Chetty et al. do try to put the “caution” labels where its needed – and its important to discuss those caution labels before some politician or two-cents-pundit goes all Trump on us by saying stuff that this doesn’t say!

A flaw regarding the chance of “out-earning” your parents

When Raj Chetty publishes a paper, it generally comes with a splash. The last one is no exception. His paper (co-authored), picked up by David Leonhardt at the New York Times and Justin Wolfers on Twitter, basically measures the American dream : what are your chances to do better than your parents. The stunning conclusion is that someone born in 1940 had a 90%+ chance of “out-earning” his parents compared with a few points above 50% for those born in the 1980s. I am not convinced. Well, when I am not convinced, I am saying I am not convincing about how big the drop is! I think the drop is smoother (the slope of decline is gentler) and the starting point for the 1940 cohort is too high.  As a big fan of Chetty, I must press this point.

More precisely, I am saying that the bar (income threshold) over which someone had to jump in 1940 is underestimated and overestimated in 1980. Setting the bar too low (high) means very high (low) chances of “out-earning” your parents. To set the bar too low, you must underestimate (overestimate) the income of the parents.  This could occur if household economies of scale are not accounted for.

An income of 30,000$ for 3 persons is not the same as an income of 60,000$ for 6 peoples. On a per capita basis, the income is the same. But, if you adjust for economies of scale in housing and furnitures, there are differences (the simplest is square root).  This gives you income per adult equivalent. Chetty et al. are aware of that and they provided a sensitivity analysis which is not mentioned by those who are relaying the article. Since household size has tended to fall over time, the growth in per capita income is faster than the growth in income per adult equivalent (a better measure). Any correction for this long-term demographic trend would attenuate the slope of the decline of the chance to out-earn your parents. And indeed, once Chetty et al. make the correction, the decline is much more modest (but still present – see below).


Simultaneously, Chetty et al. also present other important sensitivity checks. All of them relevant. But, in a strange decision, Chetty et al. decided to isolate each of the sensitivity checks rather than compile them. Taken individual, they all seem minor – except adjusting for family size. But compound this with the other sensitivity check proposed by Chetty et al.: price deflators. Using the well-known bias in the the CPI that overestimates inflation by 0.8%, Chetty et al. find that, by the end of their perod, there is roughly a ten percentage point difference between the baseline uncorrected CPI and the corrected CPI (see below). Compound this with the corrections for family and you still get a decline – but again the slope of the decline is much more modest. If you add panel B from figure 3 in Chetty et al – which includes taxes and transfers – you probably get a few extra points up. There will still probably be a decline, but a moderate one.


Finally, at footnote 19, Chetty et al. also point out that they do not account for in-kind transfers prior to 1967 (there were some).  And, on page 13, they point out that “one may be concerned that levels of absolute mobility for recent cohorts may still be understated because of increases in fringe benefits, nonmarket goods, or under-reporting of income in the CPS”. Add in all these little extra problems to the family size, the transfers and the inflation correction and I am not sure how big the drop from 1940 to the end of the studied period is. Finally, I would also add that an understudied point in economic history is what the distribution of in-kind payments according to income was. From studying the British industrial revolution, I have generally to see that it is the poorest workers who receive in-kind payments (which are not measured) and the richest receive much fewer of those in proportion of their incomes. One of the few to note that distributional was the hardcore left-leaning scholar Gabriel Kolko who mentioned this issue in Dissent back in the 1950s.  If Kolko is correct, then the income of “poor parents” in 1940 is underestimated. As a result, the bar over which the children of said parents must jump is set mildly too low. If that is the case, the odds for the 1940 birth cohort are overestimated.

Combine all of these things together and I am not sure that the drop is as dramatic as many are making it out to be. I would be very satisfied if Chetty et al. would publish all the corrections they did and do a sensitivity check with hypothetical regarding a sliding-scale of in-kind payments in 1940 according to income (10% of income for poorest to 0% for the richest). I would just like to see how much it matters.

Testing the High-Wage Economy (HWE) Hypothesis

Over the last week or so, I have been heavily involved in a twitterminar (yes, I am coining that portemanteau term to designate academic discussions on twitter – proof that some good can come out of social media) between myself, Judy Stephenson , Ben Schneider , Benjamin Guilbert, Mark Koyama, Pseudoerasmus,  Anton Howes (whose main flaw is that he is from King’s College London while I am from the LSE – nothing rational here), Alan Fernihough and  Lyman Stone. The topic? How suitable is the “high-wage economy” (HWE) explanation of the British industrial revolution (BIR).

Twitter debates are hard to follow and there is a need for summaries given the format of twitter. As a result, I am attempting such a summary here which is laced with my own comments regarding my skepticism and possible resolution venues.

An honest account of HWE

First of all, it is necessary to offer a proper enunciation of HWE’s role in explaining the industrial revolution as advanced by its main proponent, Robert Allen.  This is a necessary step because there is a literature attempting to use high-wages as an efficiency wage argument. A good example is Morris Altman’s Economic Growth and the High-Wage Economy  (see here too) Altman summarizes his “key message” as the idea that “improving the material well-being of workers, even prior to immediate increases in productivity can be expected to have positive effects on productivity through its impact on economic efficiency and technological change”. He also made the same argument with my native home province of Quebec relative to Ontario during the late 19th century. This is basically a multiple equilibria story. And its not exactly what Allen advances. Allen’s argument is that wages were high in England relative to energy. This factors price ratio stimulated the development of technologies and industries that spearheaded the BIR. This is basically a context-specific argument and not a “conventional” efficiency wage approach as that of Allen. There are similarities, but they are also considerable differences. Secondly, the HWE hypothesis is basically a meta-argument about the Industrial Revolution. It would be unfair to caricature it as an “overarching” explanation. Rather, the version of HWE advanced by Robert Allen (see his book here) is one where there are many factors at play but there is one – HWE – which had the strongest effects. Moreover, while it does not explain all, it was dependent on other factors that contributed independently.  The most common view is that this is mixed with Joel Mokyr’s supply of inventions story (which is what Nick Crafts has done). In the graph below, the “realistically multi-causal” explanation is how I see HWE. In Allen’s explanation, it holds the place that cause #1 does. According to other economists, HWE holds spot #2 or spot #3 and Mokyr’s explanations holds spot #1.


In pure theoretical terms (as an axiomatic statement), the Allen model is defensible. It is a logically consistent construct. It has some questionnable assumptions, but it has no self-contradictions. Basically, any criticism of HWE must question the validity of the theory based on empirical evidence (see my argument with Graham Brownlow here) regarding the necessary conditions. This is the hallmark of Allen’s work: logical consistency. His work cannot be simply brushed aside – it is well argued and there is supportive evidence. The logical construction of his argument requires a deep discussion and any criticism that will convince must encompass many factors.

Why not France? Or How to Test HWE

As a doubter of Allen’s theory (I am willing to be convinced, hence my categorization as doubter), the best way to phrase my criticism is to ask the mirror of his question. Rather than asking “Why was the Industrial Revolution British”, I ask “Why Wasn’t it French”. This is what Allen does in his work when he asks explicitly “Why not France?” (p.203 of his book). The answer proposed is that English wages were high enough to justify the adoption of labor-saving technologies. In France, they were not. This led to differing rates of technological adoptions, an example of which is the spinning jenny.

This argument hinges on some key conditions :

  1. Wages were higher in England than in France
  2. Unit labor costs were higher in England than in France (productivity-adjusted wages) (a point made by Kelly, Mokyr and Ó Gráda)
  3. Market size factors are not sufficiently important to overshadow the effects of lower wages in France (R&D costs over market size mean a low fixed cost relative to potential market size)
  4. The work year is equal in France as in England
  5. The cost of energy in France relative to labor is higher than in England
  6. Output remained constant while hours fell – a contention at odds with the Industrious Revolution which the same as saying that marginal productivity moves inversely with working hours

If most of these empirical statements hold, then the argument of Allen holds. I am pretty convinced by the evidence advanced by Allen (and E.A. Wrigley also) regarding the low relative of energy in England. Thus, I am pretty convinced that condition #5 holds. Moreover, given the increases in transport productivity within England (here and here), the limited barriers to internal trade (here), I would not be surprised that it was relatively easy to supply energy on the British market prior to 1800 (at least relative to France).

Condition #3 is harder to assess in terms of important. Market size, in a Smithian world, is not only about population (see scale effects literature). Market size is a function of transaction costs between individuals, a large share of which are determined by institutional arrangements. France has a much larger population than England so there could have been scale effects, but France also had more barriers to internal trade that could have limited market size. I will return to this below.

Condition #1,2,4 are basically empirical statements. They are also the main points of tactical assault on Allen’s theory.  I think condition #1 is the easiest to tackle. I am currently writing a piece derived from my dissertation showing that – at least with regards to Strasbourg – wages in France presented in Allen (his 2001 article) are heavily underestimated (by somewhere between 12% and 40% using winter workers in agriculture and as much as 70% using the average for laborers in agriculture). The work of Judy Stephenson, Jane Humphries and Jacob Weisdorf has also thrown the level and trend of British wages into doubts. Bringing French wages upwards and British wages downwards could damage the Allen story. However, this would not be a sufficient theory. Industrialization was generally concentrated geographically. If labor markets in one country are not sufficiently integrated and the industrializing area (lets say the “textile” area of Lancashire or the French Manchester of Mulhouse or the Caën region in Normandy) has uniquely different wages, then Allen’s theory can hold since what matters is the local wage rate relative to energy. Pseudoerasmus has made this point but I can’t find any mention of that very plausible defense in Allen’s work.

Condition #2 is the weakest point and given Robert Fogel’s work on net nutrition in France and England, I have no problem in assuming that French workers were less productive. However, the best evidence would be to extract piece rates in textile-producing regions of France and England. This would eliminate any issue with wages and measuring national productivity differences. Piece rates would perfectly capture productivity and thus the argument could be measured in a very straightforward manner.

Condition #4 is harder to assess and more research would be needed. However, it is the most crucial piece of evidence required to settle the issue once and for all. Pre-industrial labor markets are not exactly like those of modern days. Search costs were high which works in a manner described (with reservations) by Alan Manning in his work on monopsony but with much more frictions. In such a market, workers may be willing to trade in lower wage rates for longer work years. In fact, its like a job security argument. Would you prefer 313 days of work guaranteed at 1 shilling per day or a 10% chance of working 313 days for 1.5 shillings a day (I’ve skewed the hypothetical numbers to make my point)? Now, if there are differences in the structure of labor markets in France and England during the 18th and 19th centuries, there might be differences in the extent of that trade-off in both countries. Different average discount on wages would affect production methods. If French workers were prone to sacrifice more on wages for steady employment, it may render one production method more profitable than in England. Assessing the extent of the discount of annual to daily wages on both markets would identify this issue.

The remaining condition (condition #6) is, in my opinion, dead on arrival. Allen’s model, in the case of the spinning jenny, assumed that labor hours moved in an opposite direction as marginal productivity. This is in direct opposition to the well-established industrious revolution. This point has been made convincingly by Gragnolati, Moschella and Pugliese in the Journal of Economic History. 

In terms of research strategy, getting piece rates, proper wage estimates and proper labor supplied estimates for England and France would resolve most of the issue. Condition #3 could then be assessed as a plausibility residual.  Once we know about working hours, actual productivity and real wages differences, we can test how big the difference in market size has to be to deter adoption in France. If the difference seems implausible (given the empirical limitations of measuring effective market size in the 18th century in both markets), then we can assess the presence of this condition.

My counter-argument : social networks and diffusion

For the sake of argument, let’s imagine that all of the evidence favors the skeptics, then what? It is all well and good to tear down the edifice but we are left with a gaping hole and everything starts again. It would be great to propose a new edifice as the old one is being questioned. This is where I am very much enclined towards the rarely discussed work of Leonard Dudley (Mothers of Innovation). Simply put, Dudley’s argument is that social networks allowed the diffusion of technologies within England that fostered economic growth. He has an analogy from physics which gets the point across nicely. Matter has three states : solid, gas, liquid. Solids are stable but resist to change. Gas, matter are much more random and change frequently by interacting with other gas, but any relation is ephemeral. Liquids permit change through interaction, but they are stable enough to allow interactions to persist for some time. Technological innovation is like a liquid. It can “mix” things together in a somewhat stable form.

This is where one of my argument takes life. In a small article for Economic Affairs, I argued (expanding on Dudley) that social networks allowed this mixing (I am also expanding that argument in a working paper with Adam Martin of Texas Tech University). However, I added a twist to that argument which I imported from the work of Israel Kirzner (one of the most cited books in economics, but not by cliometricians – more than 7000 citations on google scholar). Economic growth, in Kirzner’s mind,  is the result of entrepreneurs discovering errors and arbitrage possibilities. In a way, growth is a process of discovering correcting errors. An analogy to make this point is that entrepreneurs look for profits where the light is while also trying to move the light to see where it is dark. What Kirzner dubs as “alertness” is in fact nothing else than repeated and frequent interactions. The more your interact with others, the easier it becomes for ideas to have sex. Thus, what matters is how easy it is for social networks to appear and generate cheap information and interactions for members without the problem of free riders. This is where the work of Anton Howes becomes very valuable. Howes, in his PhD thesis supervised by Adam Martin who is my co-author on the aforementioned project (summary here), showed that most innovators went in frequent with one another and they inspired themselves from each other. This is alertness ignited!

If properly harnessed, the combination of the works of Howes and Dudley (and also James Dowey who was a PhD student at the LSE with me and whose work is *Trump voice* Amazing) can stand as a substitute to Allen’s HWE if invalidated.


If I came across as bashing on Allen in this post, then you have misread me. I admire Allen for the clarity of his reasoning and his expositions (given that I am working on a funded project to recalculate tax-based measures in the US used by Piketty to account for tax avoidance, I can appreciate the clarity in which Allen expresses himself). I also admire him for wanting to “Go big or go home” (which you can see in all his other work, especially on enclosures). My point is that I am willing to be convinced of HWE, but I find that the evidence leans towards rejecting it. But that is very limited and flawed evidence and asserting this clearly is hard (as some of the flaws can go his way). Nitpicking Allen’s HWE is a necessary step for clearly determining the cause of BIR. It is not sufficient as a logically consistent substitute must be presented to the research community. In any case, there is my long summary of the twitteminar (officially trademarked now!)

P.S. Inspired by Peter Bent’s INET research webinar on institutional responses to financial crises, I am trying to organize a similar (low-cost) venue for presenting research papers on HWE assessment. More news on this later.

The Uniqueness of Italian Internal Divergence

A few weeks ago, I got engaged in a twitter debate with Garett Jones, Pseudoerasmus and Anna Missiaia (see her great work here) about institutions in Italy. During the course of that discussion, I was made aware that I held a false belief. Namely, the belief that since the late 19th century, there had only been a minor divergence within Italy. In reality, there has been considerable divergence within the country since the late 19th century.

In the wake of the Italian referendum, it is worth examining how big is this divergence. Below is a map of regional GDP per capita taken from  The southern regions of Italy have GDP per capita below 75% of the European average while some of the northern regions have GDP per capita above 125% of the European average. The IStat database suggest similar levels of divergence across regions in Italy.


So, how much divergence was there – say a little a more than one hundred years ago? Well, according to the great work of Felice (see here in the Economic History Review and here), there were more similarities back in the 19th century than there are today. Take the Liguria which – in 1891 – had per capita value added of 44% above national average. Take also Campania which was 3% below the national average. Today, the IStat data places Liguria 9% above national average but the region of Campania is 37% below the national average. Overall, regardless of how you present the data , divergence has increase. Just expressed at coefficient of variations, there has been an increase. In 1891, the coefficient of variation stood at 22.95% while it stood at 28.95% in 2013.


This makes Italy into an oddity. My own work shows that in Canada, since the 19th century, there has been considerable convergence (see article in Economics Bulletin). The same happened in the United States (see this paper by Michener and McLean), in England (here and here) and in Sweden (here). Among western countries, increased internal divergence is rare and Italy is the prime case example. And this is a strong indictment. Either Italy as a whole shares the same steady-state status and something is preventing upwards convergence from the South or Italy has two different economies with two different steady-states. In both cases, the implications are depressing.